A 3D-CNN model with CT-based parametric response mapping for classifying COPD subjects

Ho, Thao Thi; Kim, Taewoo; Kim, Woo Jin; Lee, Chang Hyun; Chae, Kum Ju; Bak, So Hyeon; Kwon, Seungwon; Jin, Gong Yong; Park, Eun Kee; Choi, Seock Hwan

doi:10.1038/s41598-020-79336-5

Cited by 51 publications

(31 citation statements)

References 37 publications

(55 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Handcrafted feature design, retrieval, and choosing are no longer required because a CNN can acquire appropriate features from raw data at multiple levels of abstraction. [13]…”

Section: Methodsmentioning

confidence: 99%

novel approach to classify skin malignancy through deep convolutional neural network and image preprocessing approaches

Sripada

2022

ijhs

View full text Add to dashboard Cite

Melanoma is a malignant skin disease that kills roughly one million people per year, as per WHO records. The proposed work's major goal is to create and develop an enhanced deep neural network model that can evaluate, recognize, and predict cutaneous skin disease in its early stages with more accuracy, lowering the chance of death. The dataset, which was gathered from dermatologists and the public domain, contains roughly 3606 images of various types of skin lesions; some of the images were distorted. Gaussian and bilateral filters were used to decrease the noise in the images. After cleaning the data, the proposed six Convolutional layered Deep-CNN networks deployed to identify and predict skin cancer. The proposed CNN network was able to recognize skin diseases such Malignant Melanoma, Squamous cellcarcinoma, and Basal cellcarcinoma with an optimal error rate and good accuracy of 98.07 percent.

show abstract

“…Handcrafted feature design, retrieval, and choosing are no longer required because a CNN can acquire appropriate features from raw data at multiple levels of abstraction. [13]…”

Section: Methodsmentioning

confidence: 99%

novel approach to classify skin malignancy through deep convolutional neural network and image preprocessing approaches

Sripada

2022

ijhs

View full text Add to dashboard Cite

show abstract

“…Communication was interrupted under this technique if the electronic equipment performed poorly. ThaoThiHo et al [38] created a 3D convolution neural network for better COPD severity estimate to map the disease severity. Finally, the time graphs representing the disease kind and affection rate are exhibited.…”

Section: Monitoring Of Body Accelerationmentioning

confidence: 99%

COPD Patient Monitoring by Virtue of Internet of Things: A Review

Sarkar¹,

Bag²

2022

YMER

View full text Add to dashboard Cite

The disease that often affects the lung by blocking the airways is known as Chronic Obstructive Pulmonary Disease (COPD). There are several cause of COPD disease and especially smoking behaviour is the major cause of chronic illness. Howeover, offering treatment for COPD patients is a challengeable task, because the symptoms and actions are not similar for all patients. To diminish this problem, the technology called Internet of Things (IoT) is been introduced to monitor the patients and submit the report with a wide range of exactness rates. Furthermore, IoT is designed with different sensor hubs to sense the patient’s body condition with high accuracy. Thus, identification of the disease severity rate is possible by monitoring several biological parameters using different processes and sensors. Analysis of parameters are performed using an efficient Machine Learning (ML) or Deep Learning (DL) approach in a suitable platform. By, using IoT technology, anybody from anywhere can able to access the medical specialist suggestion based on their body conditions. The current review article has aimed to present the uses of IoT in COPD patient monitoring in different ways as well as the function of different models are compared in the form of graphs. Keywords: Biological Parameters, Chronic Diseases, Internet of Things, Sensors, Severity Prediction

show abstract

“…The rapider the advancement of the computational performance of computer, the deeper the CNNs in sequential layers, hence, classifying object excellently. CNN-based deep learning is well accepted as an effective approach for classification [18,19], prediction [20][21][22], and object identification [23,24]. Indeed, the CNN is the predominant machine learning method for object recognition, given the robustness of it.…”

Section: Object Detectionmentioning

confidence: 99%

Application of Various YOLO Models for Computer Vision-Based Real-Time Pothole Detection

2021

View full text Add to dashboard Cite

Pothole repair is one of the paramount tasks in road maintenance. Effective road surface monitoring is an ongoing challenge to the management agency. The current pothole detection, which is conducted image processing with a manual operation, is labour-intensive and time-consuming. Computer vision offers a mean to automate its visual inspection process using digital imaging, hence, identifying potholes from a series of images. The goal of this study is to apply different YOLO models for pothole detection. Three state-of-the-art object detection frameworks (i.e., YOLOv4, YOLOv4-tiny, and YOLOv5s) are experimented to measure their performance involved in real-time responsiveness and detection accuracy using the image set. The image set is identified by running the deep convolutional neural network (CNN) on several deep learning pothole detectors. After collecting a set of 665 images in 720 × 720 pixels resolution that captures various types of potholes on different road surface conditions, the set is divided into training, testing, and validation subsets. A mean average precision at 50% Intersection-over-Union threshold (mAP_0.5) is used to measure the performance of models. The study result shows that the mAP_0.5 of YOLOv4, YOLOv4-tiny, and YOLOv5s are 77.7%, 78.7%, and 74.8%, respectively. It confirms that the YOLOv4-tiny is the best fit model for pothole detection.

show abstract

A 3D-CNN model with CT-based parametric response mapping for classifying COPD subjects

Cited by 51 publications

References 37 publications

novel approach to classify skin malignancy through deep convolutional neural network and image preprocessing approaches

novel approach to classify skin malignancy through deep convolutional neural network and image preprocessing approaches

COPD Patient Monitoring by Virtue of Internet of Things: A Review

Application of Various YOLO Models for Computer Vision-Based Real-Time Pothole Detection

Contact Info

Product

Resources

About